Improving Trial Design and Analysis for Treatments for Rare Diseases [Methods Study], 2020 (ICPSR 39118)

Version Date: Jun 10, 2024

Cite this study |

Principal Investigator(s):
Kelley Kidwell, University of Michigan. School of Public Health

https://doi.org/10.3886/ICPSR39118.v1

Version V1

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Summary

A rare disease is one that affects fewer than 200,000 people in the United States. Because few people have these diseases, clinical studies on treatments can be hard to conduct. One way to study rare disease treatments is with an small n sequential multiple assignment randomized trial (snSMART) study.

snSMART studies have two stages. In the first stage, researchers assign patients to a treatment by chance. In the second stage, patients may stay with the same treatment or switch treatments. Patients stay on the same treatment if it's working well. If the treatment isn't working, researchers assign patients by chance to a new treatment.

snSMARTs can help researchers learn more from a smaller number of patients than a standard clinical study. But most current methods for analyzing snSMARTs use data only from the first stage, which can lead to inefficient results.

In this project, the research team developed and tested new methods that use data from both stages to analyze snSMARTs. The team compared results from the new methods to actual treatment effectiveness to see:

  • Bias, or whether results are too high or too low
  • Efficiency, or how big the difference is between the results and actual treatment effectiveness

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    This study contains two supplementary documentation files. There is no data included in this release.

    Citation

    Kidwell, Kelley. Improving Trial Design and Analysis for Treatments for Rare Diseases [Methods Study], 2020 . Inter-university Consortium for Political and Social Research [distributor], 2024-06-10. https://doi.org/10.3886/ICPSR39118.v1

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    Funding

    Patient-Centered Outcomes Research Institute (PCORI) (ME-1507-31108)

    Subject Terms

    Distributor(s)

    Inter-university Consortium for Political and Social Research
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    Time Period(s)

    2020

    Date of Collection

    2020

    Data Collection Notes

    1. Users should refer to the Related Publications for additional technical information regarding the tested statistical methods and overall study design.

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    Study Purpose

    The purpose of this study was to develop new methods for designing and analyzing small n sequential multiple assignment randomized trial (snSMART) studies that increase efficiency and have low bias.

    Study Design

    The research team created test data sets with 45-300 patients that looked like data from an small n sequential multiple assignment randomized trial (snSMART) study. Using this data from both stages of a snSMART studying three treatments, researchers developed two models:

  • Bayesian joint stage model (BJSM)
  • Log Poisson joint stage model (LPJSM)

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    The models estimate the best first stage treatment based on response rates or the percentage of patients who responded to each of three treatments, from both stages. Researchers compared simulation estimates from the new models with two models that relied on data from only the first stage:

  • Bayesian first stage model (BFSM)
  • First stage maximum likelihood estimates (FSMLE)

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    To estimate response rates for treatment sequences, researchers extended the new models to allow for the second stage treatment effect to depend on the first stage treatment:

  • BJSM with multiple linkage parameters (BJSMM)
  • LPJSM with multiple parameters (LPJSMM)

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    To identify the optimal treatment sequence, researchers compared BJSMM and LPJSMM with an existing method, weighted and replicated regression method (WRRM).

    Users should refer to the Related Publications for additional technical information regarding the tested statistical methods and overall study design.

    Sample

    This study included simulated data created with sample sizes between 45 and 300 patients.

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    Original Release Date

    2024-06-10

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    Notes

    • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

    • ICPSR usually offers files in multiple formats for researchers to be able to access data and documentation in formats that work well within their needs. If you have questions about the accessibility of materials distributed by ICPSR or require further assistance, please visit ICPSR’s Accessibility Center.